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Related Concept Videos

Typical Model Studies01:30

Typical Model Studies

Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
Design Example: Creating a Hydraulic Model of a Dam Spillway01:21

Design Example: Creating a Hydraulic Model of a Dam Spillway

Scaled hydraulic models of dam spillways provide a practical way to replicate and study the intricate flow dynamics of these structures. Often built to a 1:15 ratio, these models allow for observing critical water behavior, such as velocity distribution, flow patterns, and energy dissipation.
Laminar and Turbulent Flow01:07

Laminar and Turbulent Flow

Fluid dynamics is the study of fluids in motion. Velocity vectors are often used to illustrate fluid motion in applications like meteorology. For example, wind—the fluid motion of air in the atmosphere—can be represented by vectors indicating the speed and direction of the wind at any given point on a map. Another method for representing fluid motion is a streamline. A streamline represents the path of a small volume of fluid as it flows. When the flow pattern changes with time, the streamlines...
Laminar Flow01:27

Laminar Flow

Laminar flow represents a smooth, orderly fluid motion where particles move along parallel paths, resulting in minimal mixing between layers. Streamlined particle paths characterize this flow regime and occur under conditions where viscous forces dominate over inertial forces. The distinction between laminar, transitional, and turbulent flow is primarily determined by the Reynolds number, a dimensionless quantity calculated as:
Bioreactor Controls-II01:18

Bioreactor Controls-II

In aerobic fermentations, oxygen is vital for microbial growth and metabolite production. Since air comprises only about 20% oxygen and the gas is poorly soluble in water—just 9 ppm at 20°C—supplying sufficient oxygen becomes a critical challenge, especially in high-demand processes like yeast growth or citric acid production. Even a fully saturated broth may offer only a few seconds of oxygen availability.To address this, sterile or scrubbed air is introduced into the fermentor via a sparger...

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Related Experiment Video

Updated: Jun 28, 2026

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis
06:44

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis

Published on: September 23, 2025

Interpretable machine learning and rolling-origin validation identify two distinct control and predictability regimes

Ahmed Farghaly1, Mahmoud Owais2, Ahmed M A Sattar3

  • 1Department of Civil and Environmental Engineering, College of Engineering, Majmaah University, Al-Majmaah, 11952, Saudi Arabia; Engineering Research and Applied Sciences Center, Majmaah University, Al-Majmaah, 11952, Saudi Arabia.

Journal of Environmental Management
|June 26, 2026
PubMed
Summary

This study uses machine learning to identify key factors affecting wastewater treatment in aerated lagoons. It reveals that hydraulic configuration controls solids and organic matter removal, while seasonality and influent conditions impact maturation pond performance.

Keywords:
Aerated lagoonGlobal sensitivity analysisInterpretable machine learningRolling-origin validationSeasonal forcingWastewater treatment

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Last Updated: Jun 28, 2026

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis
06:44

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis

Published on: September 23, 2025

Area of Science:

  • Environmental Engineering
  • Wastewater Treatment Technologies
  • Machine Learning Applications

Background:

  • Aerated lagoon systems are crucial for wastewater treatment, but understanding the dominant drivers of effluent quality can be challenging.
  • Operational efficiency and effluent compliance depend on accurately diagnosing process variability.
  • Existing methods may not fully capture the complex interactions influencing treatment performance over time.

Purpose of the Study:

  • To develop and apply an interpretable machine learning-global sensitivity analysis (ML-GSA) framework.
  • To identify dominant process drivers for key effluent quality indicators in a full-scale aerated lagoon system.
  • To analyze a 9-year dataset (2000-2008) for temporal trends and control mechanisms.

Main Methods:

  • Trained XGBoost surrogate models for six effluent indicators (TSS, BOD5, pH, VSS, DO, fecal coliform).
  • Coupled models with bootstrap-resolved Sobol' analysis for uncertainty-bounded sensitivity rankings.
  • Utilized rolling-origin validation to assess predictive performance and identify predictability regimes.

Main Results:

  • Identified two distinct predictability regimes: hydraulically governed outputs (TSS, BOD5) generalized forward in time, while maturation pond outputs (pH, VSS, DO, fecal coliform) showed temporal non-stationarity.
  • Confirmed hydraulic configuration as the dominant operational lever for organic matter and solids removal (Sᵀ = 0.67 for BOD5; Sᵀ = 0.94 for TSS).
  • Found maturation pond behavior dominated by seasonal and ecological factors, with 'Date' and influent VSS as key drivers for VSS, pH, DO, and fecal coliforms.

Conclusions:

  • The ML-GSA framework effectively diagnoses process drivers in complex wastewater systems.
  • Hydraulic loading and treatment stage are critical for solids and organic matter removal.
  • Seasonal variations and influent characteristics significantly influence maturation pond performance and effluent compliance.
  • Effluent limits were frequently breached, highlighting the need for optimized operational strategies, particularly concerning seasonal maturation pond disinfection.